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An introduction to the calibration of the Schwartz (1997) reduced-form. no-arbitrage two-factor model through the expectation maximization algorithm or prediction error decomposition

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An introduction to the calibration of the Schwartz (1997) reduced-form. no-arbitrage two-factor model through the expectation maximization algorithm or prediction error decomposition

no-arbitrage two-factor model through the expectation maximization algorithm or prediction error decomposition

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An introduction to the calibration of the Schwartz (1997) reduced-form. no-arbitrage two-factor model through the expectation maximization algorithm or prediction error decomposition

Autor: Carlos Armando Mejía Vega

Editorial: U. Externado de Colombia

U. Externado de Colombia

Categoría: Finanzas

Facultad: Finanzas, Gobierno y Relaciones Internacionales

Año de Edición: 2018

2018

Idioma: Español

Formato: Libro Impreso

Número de páginas: 144

ISBN: 9789587900286

9789587900286

SAP: 530005131

530005131
This book presents an introduction to the calibration (estimation of parameters) of the Schwartz (1997) reduced-forrn, no-arbirrage two factor model by applying a combination of the Kalman filter and the maximum log-likelihood method knows as the predictive error decomposition. This book is written ...
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SKU: 334650

Producto creado el 23/01/2019

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Detalles

This book presents an introduction to the calibration (estimation of parameters) of the Schwartz (1997) reduced-forrn, no-arbirrage two factor model by applying a combination of the Kalman filter and the maximum log-likelihood method knows as the predictive error decomposition. This book is written in such a way that a reader with primary tools in stochastic calculus and optimization (mainly the maximum log-Iikelihood method) can find the necessary tools for doing its reading without problems and understand the essential elements of the methodology. 

To pursue this purpose, in chapter 1 we will revise the model formally known as the Schwartz (1997) reduced-form, no-arbitrage two-factor model, and we will give some motivation for its calibration. In chapter 2, we will develop an introduction to the state space form and the general Kalman filter algorithm. In section 3, we will join the two previous chapters by applying the Kalman filter to the Schwartz (1997) reduced-form, no-arbitrage two-factor mode! under the approach of Schwartz (1997). Finally, in chapter 4 we show the optimization procedure to obtain the parameters as well as so me features and problems with it. 

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Información adicional

Editor / MarcaU. Externado de Colombia
Año de Edición2018
Número de Páginas144
Idioma(s)Español
TerminadoTapa rústica
Alto y ancho14 x 21 cm
Peso0.1500
Tipo Productolibro
custom_attributes_author~Autor~pv

Carlos Armando Mejía Vega

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custom_attributes_toc~Tabla de Contenido~pv

CONTENTS

Prologue 

CHAPTER 1 

OBSERVATIONS ON THE SCHIWARTZ (1997) REDUCED-FORM, NO-ARBITRAGE TWO-FACTOR MODEL 

1. 1 Introduction 

1. 2 Basic assumptions 

1. 2. 1 Assumption associated with the standard spot price 

1. 2. 2 Assumption associated with the net spot instanianeous convenience yield

1. 3 Presentation of the general model 

1. 4 The [uiure price dynamic over time 

1. 4.1 The risk-neutral Schwartz (1997) reduced-form, no-arbitrage two-factor model
 
1.4.2 Pricing formula
 
CHAPTER 2 
 
THE KALMAN FILTER 

2. 1 Introduction 

2. 2 The state space form 

2. 2. 1 The measurement and state vectors

2.2.2 The measurement equation 

2. 2.3 The transition equation 

2. 2. 4 Additional assumptions 

2.3 The Kalman filter 

2. 3. 1 Predict phase 

2.3. 1. 1 State prediction 

2. 3. 1. 2 Error covariance matrix prediction 

2.3. 2 Update phase 

2.3. 2. 1 Measurement or innovation residual 

2. 3. 2. 2 Innovation or residual 

covariance matrix

2.3. 2. 3 Kalman gain 

2. 3. 2. 4 State update
 
2. 3. 2. 5 Covariance update

2. 3. 3 Summary 

2. 4 Exercises 

CHAPTER 3 

THE KALMAN FILTER APPLIED TO THE SCHWARTZ (1997) REDUCED-FORM, NO-ARBITRAGE TWO-FACTOR MODEL UNDER THE SCHWARTZ (1997) APPROACH MATLAB
 
3.1 Introduction 

3.2 The state space form for the Schwartz (1997) n-rnodel under the Schwartz (1997) approach

3. 2. 1 The measurement and state vectors for the Schwartz (1997) II-model 

3. 2. 2 The measurement equation for the Schwartz (1997) I1-model
 
3. 2. 3 The transition equation for the Schwartz (1997) I1-model 3. 3 The Kalman Filter implementation 

3.3. 1 Basic elements 

3.3.2 Predict phase 

3.3. 2. 1 State prediction
 
3. 3. 2. 2 Error covariance matrix prediction 

3.3. 3 Update phase 

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